The impact of site-specific digital histology signatures on deep learning model accuracy and bias
出版年份 2021 全文链接
标题
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
作者
关键词
-
出版物
Nature Communications
Volume 12, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-07-20
DOI
10.1038/s41467-021-24698-1
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Epidemiology of Triple-Negative Breast Cancer
- (2021) Frederick M. Howard et al. CANCER JOURNAL
- Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
- (2020) Wouter Bulten et al. LANCET ONCOLOGY
- Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours
- (2020) Osamu Iizuka et al. Scientific Reports
- Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer
- (2020) Jian Carrot-Zhang et al. CANCER CELL
- Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning
- (2020) Amelie Echle et al. GASTROENTEROLOGY
- Deep learning-based survival prediction for multiple cancer types using histopathology images
- (2020) Ellery Wulczyn et al. PLoS One
- A deep learning model to predict RNA-Seq expression of tumours from whole slide images
- (2020) Benoît Schmauch et al. Nature Communications
- A machine learning-based prognostic predictor for stage III colon cancer
- (2020) Dan Jiang et al. Scientific Reports
- Integrating spatial gene expression and breast tumour morphology via deep learning
- (2020) Bryan He et al. Nature Biomedical Engineering
- DeepLRHE: A Deep Convolutional Neural Network Framework to Evaluate the Risk of Lung Cancer Recurrence and Metastasis From Histopathology Images
- (2020) Zhijun Wu et al. Frontiers in Genetics
- Deep learning in cancer pathology: a new generation of clinical biomarkers
- (2020) Amelie Echle et al. BRITISH JOURNAL OF CANCER
- Identifying transcriptomic correlates of histology using deep learning
- (2020) Liviu Badea et al. PLoS One
- Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
- (2020) Richard J. Chen et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
- (2019) Jakob Nikolas Kather et al. PLOS MEDICINE
- Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
- (2019) Jakob Nikolas Kather et al. NATURE MEDICINE
- Deep learning-based classification of mesothelioma improves prediction of patient outcome
- (2019) Pierre Courtiol et al. NATURE MEDICINE
- Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study
- (2019) Tien T. Tang et al. Scientific Reports
- Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
- (2019) David Tellez et al. MEDICAL IMAGE ANALYSIS
- An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
- (2018) Jianfang Liu et al. CELL
- Efficient deep learning model for mitosis detection using breast histopathology images
- (2018) Monjoy Saha et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
- (2018) David Tellez et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- The Immune Landscape of Cancer
- (2018) Vésteinn Thorsson et al. IMMUNITY
- Implementing Machine Learning in Health Care — Addressing Ethical Challenges
- (2018) Danton S. Char et al. NEW ENGLAND JOURNAL OF MEDICINE
- Predicting cancer outcomes from histology and genomics using convolutional networks
- (2018) Pooya Mobadersany et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Machine Learning Methods for Histopathological Image Analysis
- (2018) Daisuke Komura et al. Computational and Structural Biotechnology Journal
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- High-performance medicine: the convergence of human and artificial intelligence
- (2018) Eric J. Topol NATURE MEDICINE
- Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis
- (2017) Xin Luo et al. Journal of Thoracic Oncology
- Why Batch Effects Matter in Omics Data, and How to Avoid Them
- (2017) Wilson Wen Bin Goh et al. TRENDS IN BIOTECHNOLOGY
- Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
- (2017) Angel Cruz-Roa et al. Scientific Reports
- AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
- (2016) Shadi Albarqouni et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
- (2015) Takaya Saito et al. PLoS One
- The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014
- (2014) R. Salgado et al. ANNALS OF ONCOLOGY
- Comprehensive molecular profiling of lung adenocarcinoma
- (2014) Eric A. Collisson et al. NATURE
- scikit-image: image processing in Python
- (2014) Stéfan van der Walt et al. PeerJ
- Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data
- (2013) David A Gutman et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Removing Batch Effects From Histopathological Images for Enhanced Cancer Diagnosis
- (2013) Sonal Kothari et al. IEEE Journal of Biomedical and Health Informatics
- Population Differences in Breast Cancer: Survey in Indigenous African Women Reveals Over-Representation of Triple-Negative Breast Cancer
- (2009) Dezheng Huo et al. JOURNAL OF CLINICAL ONCOLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now